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Ethan Waldie

dblp:255/6986 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Reinforcement learning · 85% Planning, search and constraint satisfaction · 15%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
partially observable reinforcement learning		 1.022023
Learning reward machines: A study in partially observable reinforcement learning · Artif. Intell. 2023
Learning Reward Machines for Partially Observable Reinforcement Learning · NeurIPS 2019
Machine learning › Reinforcement learning › reward learning
reward machine learning		 0.712023
Learning reward machines: A study in partially observable reinforcement learning · Artif. Intell. 2023
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
problem decomposition		 0.412019
Learning Reward Machines for Partially Observable Reinforcement Learning · NeurIPS 2019
Machine learning › Reinforcement learning › reward design
reward machine		 0.412019
Learning Reward Machines for Partially Observable Reinforcement Learning · NeurIPS 2019

Methods — techniques the papers use, named apart from their topics

off-policy learning · 0.4discrete optimization · 0.4
YearPublicationVenuePosition
2023 Learning reward machines: A study in partially observable reinforcement learning
Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, Margarita P. Castro, Ethan Waldie, Sheila A. McIlraith
 Artif. Intell.5
2019 Learning Reward Machines for Partially Observable Reinforcement Learning
abstract
Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning. Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems. We pose the task of learning RMs as a discrete optimization problem where the objective is to find an RM that decomposes the problem into a set of subproblems such that the combination of their optimal memoryless policies is an optimal policy for the original problem. We show the effectiveness of this approach on three partially observable domains, where it significantly outperforms A3C, PPO, and ACER, and discuss its advantages, limitations, and broader potential.
Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Anthony Valenzano, Margarita P. Castro, Sheila A. McIlraith
 NeurIPS2